I have been working on pandemic outbreaks for 15 years.

There is a misunderstanding of the difference between the response in much of the West, versus successful countries (including New Zealand and Australia).

Summarizing:

1.Reactive versus proactive and goal oriented.

1/-

Some people really benefit from hearing advice that everyone knows, for the same reason we keep schools open despite every subject in them having been taught before.

In that spirit, here's some quick Things Many People Find Too Obvious To Have Told You Already.

Your idea is not valuable, at all. All value is in the execution. You think you are an exception; you are not. You should not insist on an NDA to talk about it; nobody serious will engage in contract review over an idea, and this will mark you as clueless.

Technologists tend to severely underestimate the difficulty and expense of creating software, especially at companies which do not have fully staffed industry leading engineering teams ("because software is so easy there, amirite guys?")

Charge more. Charge more still. Go on.

The press is a lossy and biased compression of events in the actual world, and is singularly consumed with its own rituals, status games, and incentives. The news necessarily fails to capture almost everything which happened yesterday. What it says is important usually isn't.

Companies find it incredibly hard to reliably staff positions with hard-working generalists who operate autonomously and have high risk tolerances. This is not the modal employee, including at places which are justifiably proud of the skill/diligence/etc of their employees.

1. This is "Hollie The Card".

She's a Doctor, Physician, PhD, MA, Clinical Psychologist, Hospital Consultant, PTSD consultant, Long Covid Consultant.

(Thread)

2. Although sometimes she’s also an AUTHOR and English Academic.

3. A Hospital Consultant and ex Gymnast.

(Pinterest public profile)

4. A Physician, Doctor, or hospital consultant.

A “Hospital Consultant”, would probably mean a senior doctor, in the UK at least.

5. Hollie changes her Twitter picture and bio so often, it’s hard to keep up with her qualifications.

1/ Someone emailed me asking how to break into VC, so I wanted to answer on Twitter where others could see and contribute to the conversation.

2/ “Being a VC” can mean a lot of different things, so it’s worth asking:

What actual activities do you want to do?

- Deep market analysis?
- Be in the flow of information and people?
- Make deals?
- Work closely w/ founders over time (e.g take board seats?)
- Manage capital?

3/ It’s worth specifying what type of VC you might like to become — as there are different archetypes. E.g.

- Benchmark (Lead series A/B - couple investments a year)
- First Round (Lead seed rounds, partner w/ a few companies a year)
- SV Angel (Make lots of seed investments)

4/ Continued:

Expa - Incubate companies

YC / Village Global - Build a platform to help entrepreneurs at scale

Do you want to join a firm or start one? There’s a lot to consider.

Different paths will require different skillsets & sets of experiences.

5/ Since the person who wrote the email is a young person trying to break into VC by joining a firm (and who doesn’t want to start a company), I’ll tailor this tweet storm to that goal. There’s some overlap.

1 There's a chasm between an NLP technology that works well in the research lab and something that works for applications that real people use. This was eye-opening when I started my career, and every time I talk to an NLP engineer at @textio, it continues to strike me even now.

2 Research conditions are theoretical and/or idealized. A huge problem for so-called NLP or AI startups with highly credentialed academic founders is that they bring limited knowledge of what it takes to build real products outside the lab.

3 A product is ultimately a thing that people pay for - not just cool technology or user experience. But I’m not even talking about knowledge gaps in go-to-market work. I'm talking purely technical gaps: how you go from science project to performant + delightful user experience.

4 Most commoditized NLP packages solve well-understood problems in standard ways that sacrifice either precision or performance. In a research lab, this is not usually a hard trade-off; in general, no one is using what you make, so performance is less important than precision.

5 In software, when you’re making something for real people to use, these tradeoffs are a big deal. Especially if you’re asking those people to pay for what you’ve made (can’t get away from that pesky GTM thinking). They expect quality, which includes precision AND performance.